In various types of computer systems, there may be a need to collect, maintain, and utilize confidential data. In some instances, users may be reluctant to share this confidential information due to privacy concerns. These concerns extend not only to pure security concerns, such as concerns over whether third parties such as hackers may gain access to the confidential data, but also to how the computer system itself may utilize the confidential data. With certain types of data, users providing the data may be somewhat comfortable with uses of the data that maintain anonymity, such as the confidential data merely being used to provide broad statistical analysis to other users.
One example of such confidential data is salary/compensation information. It may be desirable for a service such as a online service to request its members to provide information about their salary or other work-related compensation in order to provide members with insights into various metrics regarding salary/compensation, such as an average salary for a particular job type in a particular city. There are technical challenges encountered, however, in ensuring that such confidential information remains confidential and is only used for specific purposes, and it can be difficult to convince members to provide such confidential information due to their concerns that these technical challenges may not be met.
Additionally, viewers of reports related to the confidential data (such as aggregated salary statistics) are often interested in gaining insights on the confidential data at the organization (e.g., company) level. This, however, can be challenging in environments where there is not enough confidential data at the organization level to provide meaningful insights, such as in the case of small or medium sized organizations or in cohorts where a large organization only has a small presence in a location or title of interest (e.g., Microsoft jobs in Indianapolis or jobs as a construction worker for Google).